Computational Intelligence and Neuroscience / 2021 / Article / Tab 6

Research Article

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

Table 6

The CPU timing of the test case II by the AGD-SSC, AGD-SSC-NBC, IF, and LOF.

Seq no.AGD-SSC
mean (st. dev.)
AGD-SSC-NBC
mean (st. dev.)
IF mean (st. dev.)LOF mean (st. dev.)

Seq. 11.8829 (0.0198)2.1767 (0.0168)10.7176 (0.0397)0.9176 (0.0064)
Seq. 21.6232 (0.0606)2.8794 (0.0934)10.7297 (0.0596)0.9319 (0.0280)
Seq. 31.2637 (0.0333)2.3155 (0.0365)10.7345 (0.0681)0.9293 (0.0224)
Seq. 41.7210 (0.0357)2.2155 (0.0327)10.7328 (0.0721)0.9261 (0.0203)
Seq. 51.1815 (0.0096)2.1598 (0.0625)10.7578 (0.2188)0.9231 (0.0108)
Seq. 61.1071 (0.0366)2.2243 (0.0303)10.7458 (0.0954)0.9265 (0.0231)
Seq. 71.3799 (0.0291)2.0953 (0.0304)10.7269 (0.0342)0.9274 (0.0214)
Seq. 81.2086 (0.0247)2.0500 (0.0354)10.7376 (0.0957)0.9272 (0.0229)
Seq. 91.3493 (0.0152)2.1763 (0.0381)10.7269 (0.0455)0.9248 (0.0062)
Seq. 101.2157 (0.0347)2.1177 (0.0327)10.7687 (0.1951)0.9276 (0.0251)

Average1.39332.241110.73780.9262